Understanding survival analysis: Kaplan-Meier estimate

Abstract

Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment. In clinical trials or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. The time starting from a defined point to the occurrence of a given event, for example death is called as survival time and the analysis of group data as survival analysis. This can be affected by subjects under study that are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experienced or died if observation continued, or we lose touch with them midway in the study. We label these situations as censored observations. The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. The survival curve can be created assuming various situations. It involves computing of probabilities of occurrence of event at a certain point of time and multiplying these successive probabilities by any earlier computed probabilities to get the final estimate. This can be calculated for two groups of subjects and also their statistical difference in the survivals. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.

A Stenotrophomonas maltophilia Strain Evades a Major Caenorhabditis elegans Defense Pathway

“…Survival curves can be statistically compared by using the log-rank or Cox proportional-hazard tests. Cox proportional-hazard models were used to test the effects of independent variables such as genotype and bacterium on the hazard, a dependent variable defined as the probability of dying at a given time (38). The model used for analysis is indicated in a footnote for each table, and the effect of the designated independent variable was considered significant if the P value was Ͻ0.05.…”

CCR5del32 genotype in human enteroviral cardiomyopathy leads to spontaneous virus clearance and improved outcome compared to wildtype CCR5

“…The tremendous effect of EV persistence on mortality is underlined by the fact that the majority of these individuals (n = 10 of 15) had died within the first 6 years after initial EMB. This effect is exceedingly increased with plotting the long-term survival by censor-based, time-adjusted Kaplan–Meier calculation [26, 27]. At the end of the 15-year period, only 33% of all CCR5 wildtype patients with EV persistence without specific treatment survived.…”

Survival analysis of ART restorations in primary molars of preschool children: 1 year follow-up

“…Kaplan-Meier survival analysis was conducted in the censored data to evaluate the rhythm in which the failures occurred in different materials studied 18 . In this study, data were analyzed in two moments, at 6 and 12 months.…”